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Nghiên cứu này giải thích lý do tại sao người dùng tham gia vào việc tạo nội dung. Lý thuyết về hành vi có kế hoạch (TPB) đã được sử dụng để giải thích hành vi này.

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TẠI SAO NGƯỜI DÙNG LẠI SÁNG TẠO NỘI DUNG - ỨNG DỤNG

CỦA THUYẾT HÀNH VI CÓ KẾ HOẠCH

Zhou Xiao Hong 1 , Bùi Thị Thúy 2

Tóm tắt

Lý thuyết về hành vi có kế hoạch (TPB) kể từ khi phát triển khoảng 30 năm trước đã được chứng minh

là một cách tiếp cận mạnh mẽ để giải thích hành vi của con người Nó đã được áp dụng thành công cho một loạt các hành vi Theo lý thuyết, hành vi của người tiêu dùng là một chức năng của ý định thực hiện hành vi được đề cập; hành vi dựa trên thái độ, chuẩn mực chủ quan và kiểm soát hành vi đối với hành vi; và các yếu tố này được xác định, tương ứng, bởi thái độ đối cá nhân đối với hành vi, chuẩn mực và kiểm soát Với sự phát triển của công nghệ, nội dung do người dùng tạo ra (UGC) được coi là một phần của truyền miệng điện tử, được tạo ra và chia sẻ giữa người tiêu dùng có tầm quan trọng lớn đối với các nhà tiếp thị Nghiên cứu này giải thích lý do tại sao người dùng tham gia vào việc tạo nội dung Lý thuyết về hành vi có kế hoạch (TPB) đã được sử dụng để giải thích hành vi này Thông qua một số câu hỏi khảo sát đã được thực hiện vào tháng 10/2018, sử dụng SPSS 16 với 78 người đã được hỏi về thông tin liên quan đến nội dung được tạo và chia sẻ trên internet, kết quả kiểm tra cho thấy ý định tạo nội dung của người dùng được xác định theo thái độ cá nhân, chuẩn mực và kiểm soát

Từ khóa: Lý thuyết về hành vi có kế hoạch (TPB), nội dung do người dùng tạo ra (UGC), truyền miệng

điện tử (eWOM)

WHY USERS GENERATE CONTENT

AN APPLICATION OF THE THEORY OF PLANNED BEHAVIOR

Abstract

The theory of planned behavior (TPB) since its apprerance about 30 years ago has been proved to be a powerful approach to explain human behavior It has been successfully applied to a variety of behaviors According to the theory, the consumer’s behavior is a function of intention to perform the behavior in question; the behavior is based on attitude, subjective norm, and perceived behavioral control; and these factors are determined, respectively, by behavioral, normative, and control beliefs With the development of technology, the user-generated content (UGC) is considered as a part of electronic word of mouth created and shared between consumers, which has a major importance to marketers This study explains why users are involved in creating content The theory of planned behavior (TPB) has been used to explain this behavior Through survey questionnaires in 10/2018, using SPSS 16 with 78 respondents who were asked about information related to the generated and shared content on the internet, the results showe that the user's intention to generate content is determined by personal attitude, Subject norm, Perceived behavioral control

Keywords: Theory of planned behavior (TPB), User-generated content (UGC), electronic Word of

mouth (eWOM)

1 Introduction

Vietnam is currently ranked 7th in the

number of Facebook users with about 60 million

users Zalo currently has about 40 million

monthly users Mocha of Viettel has about 4.5

million users According to the 2017 survey

results of Pew Research Institute, Vietnamese

people ranked 4th in the world in terms of

reading news online (Trong Dat, 2018) With the

development of technology, people need to

change the way they communicate Electronic

Word-of-mouth (WOM) has been recognized as one of the most influential resources of information transmission With the Internet, even ordinary Web users can conveniently create and disseminate media content The notion of User-Generated Content captures the user-as-producer feature and refers to content that is not generated

or published by professionals on the Internet, unlike traditional media Defined in terms of situations where consumers suggest products or services to other consumers on the Internet,

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eWOM is closely related to UGC (Ye Wang,

Shelly Rodgers, 2011) UGC is related to, but not

identical with, electronic word-of-mouth(eWOM),

which is defined as being “any positive or

negativestatement made by potential, actual, or

former customers about a product or company,

which is made available to a multitude of people

and institutions via the Internet”(Hennig-Thurau

et al.2004, p 39)

The impact of user generated content is

undeniable Brand engagement also increases

when users share content According to a

ComScore study, brand engagement increases by

28% when consumers are exposed to a mixture

of branded and user-generated content

(Comscore, 2012) UGC also works wonders no

matter which generation of your target audience

When Bazaarvoice asked a pool of Millennials

and Baby Boomers how much user-generated

content played into their purchase decisions the

received answers were: 84% of Millennials said

that UGC had at least some influence; 70% of

Baby Boomers said that UGC had at least some

influence; 20%+ of each generation said that

UGC had a lot of influence (Bazaarvoice,

2012) Understanding the factors that influence

UGC creation is important for modern

marketing However, researches in this area are

limited With the application of TPB and

accreditation to find answers to this problem is

the purpose of this study

2 Background

2.1 Electronic Word-of-mouth

Social media has impacted various facets of

modern life and it has a profound influence on

interpersonal communication People need

interaction to fulfill their social needs and social

media has become a preferred medium for

communication with the proliferation of digital

and mobile technologies (Kalpathy, 2017)

People have grown up with the Internet as an

important part of their everyday life and

interaction rituals They suggest that the reason is

coming from the decrease in the amount of time

they spend interacting face-to-face (Brignall and

Van Valey, 2005) The advances in information

technology and the emergence of online social

network sites have changed the way information

is transmitted and have transcended the traditional limitations of word of mouth (Mohammad Reza, 2010)

Electronic word-of-mouth (eWOM) communication refers to any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet (T Hennig-Thurau,2004) The web has created both challenges and opportunities for electronic word-of-mouth (eWOM) communication (R E Goldsmith, 2006) eWOM allows consumers to not only obtain information related to goods and services from the few people they know but also from a vast, geographically dispersed group of people, who have experience with relevant products or services

2.2 User-Generated Content

We mention definitions and outline for our understanding of UGC, which is often referred to within the scope of Web 2.0 and social media One of the most quoted definitions of UGC is provided by the Organization for Economic Cooperation and Development (OECD) (Vickery and Wunsch-Vincent 2007) OECD uses the term

of user-created content (UCC), which is considered synonymous with UGC According to Vickery and Wunsch-Vincent (2007), UGC has three central characteristics: (1) publication requirement, (2) creative effort, and (3) creation outside of professional routines and practices Base on the features, we can see some kinds

of UGC normal in real life: Video on Youtube (Review, Parody commercials, Introduction product, Tutorial…), Picture and Video on Instagram, Post on Facebook, Twitters, Rating and comments on websites (like E-commercial Shopee, Lazada or main website of products) In the past, there have been too many successful marketing campaigns, we can mention that Old Spice- Video Responses, Coca-Cola: Share a coke, Starbucks- White Cup Contest, ect by focusing on UGC (Delhi school of internet marketing, 2016) But some of UGC became disasters such as McDonalds with hashtag #

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McDStories; Kia Sorento with creating

Kia-themed memes; Starbucks with

hashtag#SpreadTheCheer; Walgreens with

hashtag#IloveWalgreens (Duel, 2017) They are

good evidence to show up the power of UGC

2.3 Theory of planned behaviour

The Theory of Planned Behavior (TPB)

developed by Ajzen (1985) is an explanatory

model that has been widely applied in diverse

studies on behavioral intention (Lee, Cerreto & Lee, 2010 ) TPB stipulates that voluntary human behavior is preceded by intention to engage in such behavior (Shirly & Todd, 2001) Then it postulates that behavioral intention in turn is determined by three major determinants – attitude towards behavior (AB), subjective norm (SN) and perceived behavioral control (PBC)

Fig 1 Model depicting the theory of planned behavior (Ajzen, 1991)

PBC judgments are determined by beliefs

pertaining to the extent to which one has access

to resources or opportunities necessary to carry

out the behavior effectively, subjected to the

perceived power of each factor to enable or

prevent the behavior (Ajzen, 1991)

3 Conceptual framework and hypotheses

In order to develop our research framework,

we begin by examining the relationships between

each element and UGC that appear in the

literature Based on the TPB, intention signifies

the motivational components of behavior It

represents the conscious effort that a person is

willing to invest in a behavior Human action is

guided by three kinds of readily accessible

beliefs: behavioral beliefs are those about the likely consequences of the behavior, normative beliefs are those about the normative expectations and actions of important referents, and control beliefs are those about the presence

of factors that may facilitate or impede performance of the behavior (East, 2000) In their respective aggregates, behavioral beliefs bring on a favorable or unfavorable attitude (ATT) toward the behavior; normative beliefs give rise to subjective norms (SN) or perceived social pressure (which also contribute to the forming of attitudes), and control beliefs result in perceived behavioral control (PBC)

Fig 2 Proposed research model

Attitude

Subject norm

Perceived Behaviour

control

Attitude to UGC

Subject norm

Perceived Behaviour

control

Intention to create UGC

Create UGC

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So, base on TPB model, we suggest below

hypotheses:

H1: Consumer UGC attitude affects content

creation on the network

H2: Social influence has effect on the

content creation behavior on the network

H3: Behavior control affects the content

creation behavior on the network

4 Methodology

There is no official TPB questionnaire,

however, base on the original research (Ajzen,

1991) and instrument of "Constructing a theory

of planned behavior questionnaire" and sample

questionnaire are provided by Ajzen (2013) we

developed the questionnaire for this research

We focus on a behavior creat video upload

to facebook then interaction between them and

their facebook friend To measure for

“Behavior”, we create 3 questions about

frequency of the action record, create Video, Edit

Video and Share Video about a product We use

Likert 5 point scales with 1= rarely and

5=Usually for measurement behavior The

"Attitude" scale consists of 3 items that reflect

the implementation of actions towards action, the

respondents feel interesting, value, happy when

share information, we use Likert 5 point scales

with this variation “Subject norm” shows the

reaction toward the action by other people from

the community, they might friends, another

member from society, so we develop 3 questions

your friend always creates and share contents on

the Internet, your friend react positively

whenever you share contents on the Internet,

your friend appreciated your contents, we use

Likert 5 point scales with this variation with 5 is

highest point for positive reaction “Control”

mention the ability of respondents when we tend

to do the behavior So in this case, we develop 3

questions to describe the ability of the user, do

they meet any difficulty when they want to do

the action which is showing up through they

photograph skills, supporting equipment, Editing

skills And we use Likert 5 point scales with this

variation with 5 was for master skills and 1 for

novice skills

A questionnaire was developed for use in

the data collection process with 12 questions

directly related to our research Measurements

behavioral control are adjusted from previous studies For each element designed with three questions, we used the 5-point Likert scale for the study In the process of developing a questionnaire, we refer to studies that apply TPB

in explaining other human behavior

The SPSS analysis process applied to the thesis applied a lot of formulas Among them, the formula for determining the minimum sample size for research is reliable The size of the sample applied in the study is based on the requirements of the Exploratory Factor Analysis (EFA) and the multivariate regression Based on research by Hair, Anderson, Tatham and Black for reference on expected sample size, accordingly the minimum sample size is 5 times the total number of observed variables This is a suitable sample size for the study using factor analysis (Comrey, 1973; Roger, 2006) n = 5 *

m, note that m is the number of questions in the lesson So the accepted minimum is 60 For multivariate regression analysis: With Tabachnick and Fidell fomular, the minimum sample size to be obtained is calculated by the formula n = 50 + 8 * m=74 A total of 78 questionnaires were distributed to respondents on Facebook The data were then analyzed using SPSS version 16 Descriptive analysis, reliability analysis, factor analysis and regression analysis were then performed on the data

Demographic

A total of 78 respondents from Facebook community, majority of the respondents were female (43 respondents or 55.1%), between the ages of 18 and 30 years of age ( 38 respondents or 48.7%), most of them are high school students or not who have just only finished high school or not graduated from high school yet (40 respondents or 51.3%) The background of the respondents is presented in bellow table ( Table 1)

5 Result

5.1 Reliability statistic Cronbach’s Alpha

Cronbach‟s Alpha of them from 0.6 to 0.9 (Behavior- 0.845; Attitudes - 0.821; Subjective Norm – 0.836; Control - 0.780) and Corrected item- Total correlation >0.4 so and Cronbach‟s Alpha if item Deleted isn‟t bigger than the Total Cronbach Alpha so don‟t need to delete any question (Table 1)

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Table 1: Reliability statistic Cronbach’s Alpha

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item Deleted

Cronbach's Alpha

0,845

0,821

0,836

0,78

5.2 Exploratory factor analysis

0.5 ≤ KMO≤ 1: KMO coefficient

(Kaiser-Meyer-Olkin) is an index used to consider the

appropriateness of factor analysis In this

research KMO=0.756 values mean that factor

analysis is appropriate.( Table 5.2.1) The Bartlett

test has statistical significance (Sig <0.05): This

is a statistical quantity used to consider the

hypothesis that variables are not correlated in the

overall This test is statistically significant (

0.000 <0.05) so the observed variables are

correlated with each other in the overall.( Table

5.2.1) With 9 input variables, PCA initially

extracts 9 factors (or “components”) Each

component has a quality score called an Eigenvalue Only components with high Eigenvalues are likely to represent a real underlying factor A common rule of thumb is to select components whose Eigenvalue is at least

1 So our 9 variables seem to measure 3 underlying factors (Table 5.2.2) Percentage of variance 74.088% > 50%: Shows the percentage variation of observed variables This means that when the variable is 100%, the value indicates 74.088% the factor analysis explains Factor loading each item > 0.5 is considered to have practical significance (Table 2)

Table 2: Total Variance Explained

Component

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings Total % of

Variance

Cumulative % Total

% of Variance

Cumulative

% of Variance

Cumulative %

1 3,745 41,61 41,61 3,745 41,61 41,61 2,281 25,346 25,346

2 1,599 17,762 59,372 1,599 17,762 59,372 2,229 24,77 50,116

3 1,324 14,717 74,088 1,324 14,717 74,088 2,158 23,972 74,088

Extraction Method: Principal Component Analysis

We use Rotated Component Matrix as below, results show ATT, SN, TBC are Convergent Validity

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Table 3: Rotated Component Matrix

Rotated Component Matrix a

Component

5.3 Testing hypothesises

Adjusted R Square, also known as R square

correction, it reflects the degree of influence of

the independent variables on the dependent

variable Specifically, in this case, 3 independent

variables affect 59.4% of the variation of the

dependent variable, the remaining 40.6% is due

to out-of-model variables and random errors

This value is more than 50%, the study can be

used If d > dU,α, there is no statistical evidence that the error terms are positively autocorrelated( Durbin-Watson Significance Tables) To test for positive autocorrelation at significance α, the test statistic d is compared to lower and upper critical values (dL,α and dU,α): 4-dL>d > dU (4-1.57>2.112>1.72),α, there is no statistical evidence that the error terms are positively or negative autocorrelated (Table 3)

Table 4: Model summarary

Model R R Square Adjusted R Square Std Error of

the Estimate Durbin-Watson

a Predictors: (Constant), TBC, SN, ATT

b Dependent Variable: BH

For VIF (variance inflation factor) for each

item <2 is not multicollinear and Sig <0.05 so

the hypothesises are supported (Table 5.3.2)

Hypotheses

H1: Consumer UGC attitude affects content

creation on the network (Supported)

H2: Social influence has effect on the

content creation behavior on the network

(Supported)

H3: Behavior control affects the content

creation behavior on the network (Supported)

6 Conclusion and Limitation

Theoretically, this study lends support to the

theory of planned behavior in explaining

intention to generate digital information as UGC

All the factors; attitude, subjective norms and

perceived behavioral control, all of them were

tested to be positively influenced the intention to

users generate content This indicates that

attitude, subjective norms and perceived behavioral control were predictors of intention to use digital coupon Overall, these factors explained about 59.4% of the variance in intention while the remaining 40.6% may be explained by other factors that were not captured

in this model The attitude was found to be the strongest predictor of intention to use generate content followed by perceived behavioral control and subjective norms

Limitations

This study faced a number of limitations Firstly, data for this study were obtained from a sample including 78 people If all the large samples size were examined, the result could have been generalized Secondly, the study focused only on the consumer behavioral intention, but actual usage was not measured

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Thông tin tác giả:

1 Zhou Xiao Hong

- - Đơn vị công tác: Professor in Nanjing University of Science and Technology

2 Bùi Thị Thúy

- - Đơn vị công tác: Student of Nanjing University of Science and Technology

- Địa chỉ email: buithithuy.neu@gmail.com

Ngày nhận bài: 12/10/2018 Ngày nhận bản sửa: 2/11/2018 Ngày duyệt đăng: 28/12/2018

Ngày đăng: 25/10/2020, 19:58

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